Bringing legal work in-house, explained.
Guides for the teams that own the matter.
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Azure AI Foundry (now Microsoft Foundry) for legal teams: a CIO's primer
Microsoft Foundry, formerly Azure AI Foundry, is Microsoft's platform for running AI models inside an Azure subscription. When a legal AI platform deploys in your own tenant, your documents, models and sign-in stay under your existing governance, your data stays out of model training, and Microsoft bills the usage at list price.
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Per-GB vs. per-matter pricing: a CFO-friendly comparison
Per-gigabyte eDiscovery pricing ties your bill to data volume you can't know until processing is done, and hosting fees keep running for the life of the matter. Per-matter pricing sets the price by the complexity of the matter before work starts. For budgeting, per-matter is the more predictable model.
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What "defensible AI review" actually means in practice
Defensible AI review means you can explain and support every review decision if opposing counsel, a court, or a regulator asks. In practice that takes a written reason for each call cited to the source document, a log of every agent and reviewer action, human sign-off where stakes are high, and quality checks you can describe.
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The economics of bringing litigation document review in-house
First-level document review is the most expensive phase of most matters, and the one legal teams send out most often. Bringing it in-house on an agentic review platform cuts the cost of a representative matter by roughly half, runs about 20× faster than manual or TAR review, and leaves a defensible record your team owns.